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Artificial Intelligence Data Analysis Data Science

Is AI Changing the 80/20 Rule of Data Science?

Cleaning and optimizing data is one of the biggest challenges that data scientists encounter. The ongoing concern about the amount of time that goes into such work is embodied by the 80/20 Rule of Data Science. In this case, the 80 represents the 80% of the time that data scientists expend getting data ready for use and the 20 refers to the mere 20% of their time that goes into actual analysis and reporting.

Much like many other 80/20 rules inspired by the Pareto principle, it’s far from an ironclad law. This leaves room for data scientists to overcome the rules, and one of the tools they’re using to do it is AI. Let’s take a look at why this is an important opportunity and how it might change your process when you’re working with data.

The Scale of the Problem

At its core, the problem is that no one wants to be paying data scientists to prep data anymore than is necessary. Likewise, most folks who went into data science did so because deriving insights from data can be an exciting process. As important as diligence is to mathematical and scientific processes, anything that allows you to do more diligence and to get the job done faster is always a win.

IBM published a report in 2017 that outlined the job market challenges that companies are facing when hiring data scientists. Growth in a whole host of data science, machine learning, testing, and visualization fields was in the double digits year-over-year. Further, it cited a McKinsey report that shows that, if current trends continue, the demand for data scientists will outstrip the job market’s supply sometime in the coming years.

In other words, the world is close to arriving at the point where simply hiring more data scientists isn’t going to get the job done. Fortunately, data science provides us with a very useful tool to address the problem without depleting our supply of human capital.

Is AI the Solution?

It’s reasonable to say that AI represents a solution, not The Solution. With that in mind, though, chipping away at the alleged 80% of the time that goes into prepping data for use is always going to be a win so long as standards for diligence are maintained.

Data waiting to be prepped often follow patterns that can be detected. The logic is fairly straightforward, and it goes as follows:

Have individuals prepare a representative portion of a data set using programming tools and direct inspections.

Build a training model from the prepared data.

Execute and refine the training model until it reaches an acceptable performance threshold.

Apply the training model and continue working on refinements and defect detection.

Profit! (Profit here meaning to take back the time you were spending on preparing data.)

There are a few factors worth considering. First, depending on the size of the task and its overall value, it has to be large enough that a representative sample can be extracted from the larger dataset. Preferably, you don’t want it to be 50% of the overall dataset, otherwise, you might be better off just powering through with a human/programmatic solution.

Second, some evidence needs to exist that shows the issues with each dataset lend themselves to AI training. While the power of AI can certainly surprise data scientists in terms of improving processes such as cleaning data as well as finding patterns, you don’t want to be on it without knowing that upfront. Otherwise, you may spend more time working with the AI than you gain for doing analysis.

Conclusion

The human and programming elements of cleaning and optimizing data will never go away completely. Both are essential to maintaining appropriate levels of diligence. Moving the needle away from 80% and toward or below 50%, however, is critical to fostering continued growth in the industry. 

Without a massive influx of data scientists into the field in the coming decade, something that does not appear to be on the horizon, AI is one of the best hopes for turning back the time spent on preparing datasets for analysis. That makes it an option that all projects that rely on data scientists should be looking at closely.

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Data Science Data Science Careers

Citizen Data Scientist vs. Data Scientist: What’s the Difference?

Over the past decade, businesses and organizations have come to rely on the competitive edge afforded by predictive analytics, business modeling, and behavioral marketing. And these days, enlisting both data scientists and citizen data scientists to optimize information systems is an effective way to save money and squeeze the most from data sets.

What is a Citizen Data Scientist?

Citizen data scientist is a relatively new job description. Also known as CDSs, they are low- to mid-level “software power users” with the skills to handle rote analysis tasks. Typically, citizen data scientists use WYSIWYG interfaces, drag-and-drop tools, in addition to pre-built models and data pipelines.

Most citizen data scientists aren’t advanced programmers. However, augmented analytics and artificial intelligence innovations have simplified routine data prep procedures, making it possible for people who don’t have quantitative science backgrounds to perform a scope of tasks.

Except in the rarest of circumstances, citizen data scientists don’t deal with statistics or high-level analytics.

At present, most companies underutilize CDSs. Instead, they still hire experts, who command large salaries or consulting fees, to perform redundant tasks that have been made easier by machine learning.

What is a Data Scientist?

Data scientists — also known as expert data scientists — are highly educated engineers. Nearly all are proficient in statistical programming languages, like Python and R. The overwhelming majority earned either master’s degrees or PhDs in math, computer science, engineering, or other quantitative fields.

In today’s market — where data reigns supreme — computational scientists are invaluable. They’re the brains behind complex algorithms that power behavioral analytics and are often enlisted to solve multidimensional business challenges using advanced data modeling. Expert data scientists work with structured and unstructured objects, they also often devise automated protocols to collect and clean raw data.

Why Should Companies Use Both Expert and Citizen Data Scientists?

Since CDSs cost significantly less than qualified scientists, having both citizen and expert data engineers in the mix saves money while allowing your business to maintain a valuable data pipeline. Plus, data engineers are in short supply, so augmenting their support staff with competent CDSs is often a great solution.

Some companies outsource all their data analytics needs to a dedicated third party. Others recruit citizen data scientists from within their ranks or hire new employees to fill CDS positions.

How to Best Leverage Citizen Data Scientists and Expert Data Scientists

Ensuring your data team hums along like a finely tuned motor requires implementing the five pillars of productive data work.

  1. Document an Ecosystem for CDSs: Documenting systems and protocols makes life much easier for citizen data scientists. In addition to outlining personnel hierarchies, authorized tools, and step-by-step data process rundowns, the document should also provide a breakdown of the company’s goals and how CDS work fits into the puzzle.
  2. Augment Tools: Instead of reinventing the wheel, provide extensions to existing programs commonly used by citizen data scientists. The best augmentations complement CDS work and support data storytelling, preparation, and querying.
  3. Delegate: Pipelines that use both expert and citizen data scientists work best when job responsibilities are clearly delineated. Tasks that require repetitive decision-making are great for CDSs, and the experts should be saved for complex tasks.
  4. Communication: Communication is key. Things run smoother when all levels share results and make everyone feel part of the team.
  5. Trash the Busy Work: People perform better when they feel useful. Saddling citizen data scientists with a bunch of busy work that never gets used is a one-way road to burnout — and thus a high turnover rate. Try to utilize every citizen data scientist to their highest ability.

Implementing a Comprehensive Data Team

Advancements in machine learning have democratized the information industry, allowing small businesses to harness the power of big data.

But if you’re not a large corporation or enterprise — or even if you are — hiring a full complement of expert and citizen data scientists may not be a budgetary possibility.

That’s where data analysis software and tools — like Inzata Analytics — step in and save the day. Our end-to-end platform can handle all your modeling, analytics, and transformation needs for a fraction of the cost of adding headcount to your in-house crew or extensive tech stacks. Let’s talk about your data needs. Get in touch today to kick off the conversation. If you want your business to profit as much as possible, then leveraging data intelligence systems is the place to start.

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Data Science

What is NoSQL? Non-Relational Databases Explained

Non-tabular NoSQL databases are built on flexible schemas, with nested data structures, to accommodate modern applications and high user loads. Increasingly, they’re the optimal choice when ease of development, scaled performance, speed, and functionality are central operating concerns.

What is NoSQL?

NoSQL stands for “not only SQL” to signify that it accommodates various data access and management models. “Nonrelational database” is also interchangeable with “NoSQL database.” Programmers developed the language in the mid-2000s alongside the widespread adoption of new information trends. 

Some people insist that NoSQL databases are sub-par when it comes to storing relational data. However, this is an unfair argument. Nonrelational databases can handle relationship data; it’s just done differently. Plus, other folks may argue that relationship modeling is more manageable in the NoSQL environment because data isn’t parsed between multiple tables.

What? Can You Please Explain in Non-Tech Talk?

Does this all sound like Greek to you? If so, think of SQL and NoSQL as vinyl records and digital music streaming, respectively. To play a song on a vinyl album, you must physically move the needle on the turntable to access the “track” you want because the record was pressed ahead of time, and that’s where the desired “files” reside. Thanks to remote access memory, however, when streaming digital music, you can just press or click to play a song — even though all the parts making up the file may be scattered.

It’s not a perfect analogy, but the technological gist falls into the same category.

A Brief History of NoSQL

Back in the second half of the 20th century, when digital computing was still in its infancy, relatively speaking, data storage was one of the most expensive aspects of maintaining data sets. But by the late 2000s, on the back of Moore’s Law, those costs had plummeted, and developers’ fees were instead topping cost lists.

Back then, social media and digital networking were skyrocketing in popularity, companies were inundated with mounds of raw data, and programmers were adopting the Agile Manifesto, which stressed the importance of responding to change instead of remaining slaves to procedural protocol. As such, new tools were developed to accommodate the influx of information and the need to be highly flexible.

The practical and philosophical shift served as an inflection point and changed the course of computing history. Developer optimization was prioritized, flexibility took priority, and the confluence of events led to the creation of NoSQL databases.

NoSQL v. SQL

Before NoSQL, there was SQL — the querying language used for relational database environments where tables are constrained and connected by foreign and primary keys.

On the other hand, NoSQL allows for greater flexibility and can operate outside the confines of a relational database on newer data models. Plus, since NoSQL-friendly databases are highly partitionable, they’re much easier to scale than SQL ones. 

To be clear, SQL is still used today. In fact, according to reports, as of May 2021, approximately 73 percent of databases run on the SQL model — but NoSQL is rapidly expanding its market share.

Why Do Developers Use NoSQL?

Developers typically use NoSQL for applications that process large amounts of data and require a low latency speed. It’s achieved by easing up on consistency restrictions and allowing for different data types to commingle.

Types of NoSQL Databases

There are five main types of NoSQL databases.

  • Key-Value: Key-value databases can be broken up into many sections and are great for horizontal scaling.
  • Document: Document databases are great when working with objects and JSON-like documents. They’re frequently used for catalogs, user profiles, and content management systems.
  • Graph: Graph databases are best for highly connected networks, including fraud detection services, social media platforms, recommendation engines, and knowledge graphs.
  • In-Memory: In-memory databases are ideal for apps requiring real-time analytics and microsecond responses. Examples include ad-tech and gaming apps that feature leaderboards and session stores.
  • Search: Output logs are an important part of many apps, systems, and programs. Search-based NoSQL databases streamline the process.

In a nutshell, NoSQL is a database revolution that’s helping drive tech innovation.

Categories
Data Analysis Data Science

7 Ways to Optimize Your SQL Queries for Production Databases

According to Google, 53 percent of mobile users will abandon a website if it doesn’t load within three seconds — and the bounce rates for PCs and tablets aren’t that much more. So what do these stats mean for coders? Ultimately, they’re a stark reminder that crafting optimized SQL queries for production database environments should be a priority.

What Are SQL Queries and Production Databases?

New programmers may be wondering: What are SQL queries and production databases? At first, the terms may sound intimidating. But the reality is simple: SQL queries are simply the code you write to extract desired records from a database, and a production database just means “live data.”

In other words, a dynamic website that’s live and accessible is likely working off a production database.

Why Should SQL Queries Be Optimized?

Which would you rather read: a loquacious tome freighted with filler words and pretentious tangents or a tl;dr summary that zeros in on the topic at hand? Moreover, which would take longer to digest? 

The same keep-it-simple logic applies to writing in SQL: the best queries are short, sweet, and get the job done as quickly as possible — because cumbersome ones drain resources and slow loading times. Plus, in the worst-case scenarios, sloppy queries can result in error messages, which are UX kryptonite.

Seven Ways to Optimize SQL Queries for Production Databases

#1: Ask the Right Questions Ahead of Time

Journalists have long understood the importance of who, what, when, where, and how. Effective coders also use the five questions as a framework. After all, every business has a purpose, and, like a finely tuned car, every mechanism should support the company’s ultimate goal — even the SQL queries that power its websites, databases, and reporting systems.

#2: Only Request Needed Data

One significant difference between novice programmers and experienced ones is that individuals in the latter category write elegant queries that only return the exact data needed. They use WHERE instead of HAVING to define filters and avoid deploying SELECT DISTINCT commands unless absolutely necessary.

#3: Limit Sources and Use the Smallest Data Types

If you don’t need a full report of matching records, or you know the approximate number of records that a query should return, use a LIMIT statement. Also, make sure to use the smallest data types; it speeds things up.

#4: Be Minimalist, Mind Indexes, and Schedule Wisely

Choose the simplest and most elegant ways to call up needed data. To state it differently, don’t over-engineer. Moreover, make use of table indexes. Doing so speeds up the query process. Plus, if your network includes update queries or calls that must be run daily, schedule them for off-hours!

#5: Consider Table Sizes

Joining tables is an SQL query staple. When doing it, make sure to note the size of each table and always link in ascending order. For example, if one table has 10 records and the other has 100, put the former first. Doing so will return the desired results and cut down on query processing time.

#6: Only Use Wildcards at the End

Wildcards can be a godsend, but they can also make SQL queries unruly. By placing them at the beginning and end of a variable, you’re inefficiently forcing the broadest possible search. Instead, get specific. And if you must use a wildcard, make sure it’s at the end of a statement.

#7: Test to Polish

Before you put a project to rest, test! Try different combinations; whittle away at your queries until they’re elegant code blocks that make the least number of database calls. Think of testing as the editing stage, and revise until the work is polished.

Who Should Tweak SQL Queries?

People with little or no coding experience may be able to DIY a small CSS change or add an XHTML element without catastrophe. But SQL queries are a very different story, one errant move can wreak mayhem across your operations.

Optimizing SQL queries is essential in today’s digital landscape, and failing to do so can lead to decreased views and profits. So make sure to optimize the code before going live. And if you don’t have the experience, enlist the help of someone who does. It’s worth the investment.

Categories
Business Intelligence Data Analysis Data Science

Data Science vs. Business Intelligence: What’s the Difference?

In today’s business world, it seems like all decisions and strategies ultimately point back to one thing: data. However, how that data is being used to find value and produce insights from within the data stack is a different story. Business intelligence and data science are two terms often used interchangeably when talking about the who, what, why, and how of working with data. 

While they both appear to work with data to solve problems and drive decision-making, what’s the real difference between the two? Let’s get back to the basics by diving into the similarities and differences of each when it comes to their core functions, deliverables, and overall role as it relates to data-driven decision-making.

What is Business Intelligence?

Business intelligence is developing and communicating strategic insights based on available business information to support decision-making. The purpose of business intelligence is to provide a clear understanding of an organization’s current and historical data. When BI was first introduced in the early 1960s, it was designed as a method of communicating information across business units. Since then, BI has evolved into advanced practices of data analysis but communication has remained at its core.

Additionally, BI is much more than processes and methods for analyzing data or answering specific business questions, it also includes the technologies behind those methods. These tools, often self-service, allow users to quickly visualize and understand business information.

Why is Business Intelligence Important?

Since data volumes are rapidly increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This gives guidance towards informed decision-making and identifying areas of improvement, leading to greater organizational efficiency and an increased bottom line.

What is Data Science?

While there is no universally accepted definition of data science, it’s generally accepted as a field that embraces many disciplines, including statistics, advanced programming skills, and machine learning, in order to generate actionable insights from raw data. 

In simple terms, data science is the process of obtaining value from a company’s data, usually to solve complex problems. It’s important to note that data science is still developing as a field and this definition is continually evolving with time.

Why is Data Science Important?

Data science is a guide through which companies are able to predict, prepare, and optimize their operations. Moreover, data science can be pivotal to the user experience, for many businesses data science is what allows them to offer personalized and tailored services. For instance, streaming services, such as Netflix and Hulu, are able to recommend entertainment options based on the user’s previous viewing history and taste preferences. Subscribers spend less time searching for what to watch and are able to easily find value amongst the hundreds of offerings, giving them a unique and personally curated experience. This is significant in that it increases customer retention while also enhancing the subscriber’s ease of use. 

Business Intelligence vs. Data Science: What’s the Difference?

Generally speaking, business intelligence and data science both play a key role in producing any organization’s actionable insights. So where exactly is the line between the two? When does business intelligence end and data science begin?

BI and data science vary in a number of ways, from the type of data they’re working with to project deliverables and approaches. See the figure below for a visual distinction between the most common attributes of the two.

Perspective

Business intelligence is focused on the present while data science is looking towards the future and predicting what might happen next. BI works with historical data in order to determine a responsive course of action while data science creates predictive models that recognize future opportunities.

Data Types

Business intelligence works with structured data that is typically data warehoused or stored in data silos. Similarly, data science also works with structured data but predominantly is tasked with unstructured and semi-structured data, resulting in greater time dedicated towards cleaning and improving data quality.

Deliverables

Reports are the name of the game when it comes to business intelligence. Other deliverables for business intelligence include things like building dashboards and performing ad-hoc requests. Data science deliverables have the same end goal in mind but focus heavily on long-term and forward-looking projects. Projects will include building models in production rather than working from enterprise visualization tools. These projects also place a heavyweight on predicting future outcomes as opposed to BI’s focus on an organization’s current state.

Process

The distinction between the processes of each comes back to the perspective of time, similarly to how it influences the nature of deliverables. Business intelligence revolves around descriptive analytics, this is the first step of analysis and sets the stage for what has already happened. This is where non-technical business users can understand and interpret data through visualizations. For example, business managers can determine how many of item X was sold in July from promotional emails versus through direct website traffic. This then leads to additional digging and analysis regarding why some channels performed better than others. 

Continuing with the previous example of item X, data science would take the exploratory approach. This means investigating the data through its attributes, hypothesis testing, and exploring common trends rather than answering business questions on performance first. Data scientists often start with a question or complex problem but this typically evolves upon exploration.

How Do BI & Data Science Drive Decisions?

While business intelligence and data science are both used to drive decisions, their perspective is central to determining the nature of decision-making. Due to the forward-looking nature of data science, it’s most often at the forefront of strategic planning and determining future courses of action. These decisions, though, are often preemptive rather than responsive. On the other hand, business intelligence aids decision-making based on previous performance or events that have occurred. Both disciplines fall under the umbrella of providing insights that will support business decisions, but the element of time is what distinguishes the two.

However, it’s important to note that this might not always be the case for every organization. The lines between the responsibilities of BI and data science teams are often blurred and vary from organization to organization.

Conclusion

Despite their differences, the end goal of business intelligence and data science is ultimately aligned. It’s important to note, though, the complementary perspectives of the two. Examining the past, present, and future through data remains vital to staying competitive and addressing key business problems.

Categories
Big Data Data Analytics Data Science

7 Bad Habits Every Data Scientist Should Avoid

Are you making these mistakes? As a data scientist, it can be easy to fall into some common traps. Let’s take a look at the most common bad habits amongst data scientists and some solutions on how to avoid them. 

1. Not Understanding the Problem

Ironically, for many data scientists, understanding the problem at hand is the problem itself. The confusion here often occurs for a couple of reasons. Either there is a disconnect between the data scientist’s perspective and the business context of the situation or the instructions given are very vague and ambiguous. These reasons all lead back to a lack of information and understanding of the situation.

Misunderstandings of the business case can lead to wasted time spent working towards the wrong approach and often causes many unnecessary headaches. Don’t be afraid to ask clarifying questions, having a clear picture of the business problem being asked is vital to your efficiency and effectiveness as a data scientist. 

2. Not Getting to Know Your Data

We’re all guilty of wanting to jump right in and get the ball rolling, especially when it comes to a shiny new project. This ties into the last behavioral point, rushing to model your data without fully understanding its contents can create numerous problems in itself. A thorough and precise exploration of the data prior to analysis can help determine the best approach to solving the overarching problem. As tempting as it may be, it’s important to walk before you can run.  

After all, whatever happened to taking things slow? Allocate time for yourself early on to conduct an initial deep dive. Don’t skip over the getting to know you phase and jump right into bed with the first model you see fit. It might seem counterintuitive but taking time to get to know your data at the beginning can help save time and increase your efficiency later down the line. 

3. Overcomplicating Your Model

Undoubtedly, you will face numerous challenges as a data scientist, but you will quickly learn that a fancy and complicated model is not a one size fits all solution. It’s common for a complex model to be a data scientists’ first choice when diving into a new project. The bad habit, in this case, is starting with the most complex model when a more simple solution is available. 

Try starting with the most basic approach to a problem and expand your model from there. Don’t overcomplicate things, you could be causing yourself an additional headache with the time drained into the more intricate solution.

4. Going Straight for the Black Box Model

What’s worse than diving in headfirst with an overly complex model? Diving in headfirst with a complex model you don’t entirely understand. 

Typically, a black box is what a data scientist uses to deliver outputs or deliverables without any knowledge of how the algorithm or model actually works. This happens more often than one might think. Though this may be able to produce effective deliverables, it can also lead to increased risk and additional problems. Therefore, you should always be able to answer the question of “what’s in the box?” 

5. Always Going Where No One Has Gone Before

Unlike the famous Star Trek line, you don’t always have to boldly go where no man has gone before in the realm of data science. While being explorative and naturally curious when it comes to the data is key to your success, you will save a lot of time and energy in some cases by working off of what’s already been done.

Not every model or hypothesis has to be a groundbreaking, one of a kind idea. Work from methods and models that other leaders have seen success with. Chances are that the business questions you’re asking your data or the model you’re attempting to build have been done before. 

Try reading case studies or blog posts speaking on the implementation of specific data science projects. Becoming familiar with established methods can also give you inspiration for an entirely new approach or lead you to ideas surrounding process improvement.

6. Doing It All Yourself

It’s easy to get caught up in your own world of projects and responsibilities. It’s important, though, to make the most of the resources available to you. This includes your team and others at your organization. Even your professional network is at your disposal when it comes to collecting feedback and gaining different perspectives. 

If you find yourself stuck on a particular problem, don’t hesitate to involve key stakeholders or those around you. You could be missing out on additional information that will help you to better address the business question at hand. You’re part of a team for a reason, don’t always try to go it alone!

7. Not Explaining Your Methods

The back end of data science projects might be completely foreign to the executive you’re working within marketing or sales. However, this doesn’t mean you should just brush over your assumptions and process to these non-technical stakeholders. You need to be able to explain how you got from point A to point B, how you built your model, and how you ultimately produced your final insights in a way that anyone can understand.

Communication is essential to ensure the business value is understood and properly addressed from a technical standpoint. Though it might be difficult to break things down in a way that non-technical stakeholders can understand, it’s important to the overall success of any project you will work on. This is where storytelling tactics and visualizations can come in handy and easily allow you to communicate your methods.

Categories
Data Science Data Science Careers

5 In-Demand Traits of Highly Effective Data Scientists

Demand has consistently been on the rise for data science roles and analytics skills across all industries. In 2021, it’s predicted that there will be an additional 3,037,809 new job openings in data science worldwide as companies move to become data-driven.

Whether you’re an aspiring data scientist yourself or just looking to acquire the mindset of one, knowing the essential qualities it takes to succeed in the role can help highlight what to focus on in your development. This leads us to the question: What does it take to be a successful data scientist? Here are the traits that set effective data scientists apart from the rest.

Business Vision

The most successful data scientists commonly have the ability to understand the company’s situation from a business standpoint and always keep the organization’s overarching goals in mind. This is important to understand the why behind the data. Business acumen is the key to determining what critical business questions the data is looking to answer or what questions need to be asked.

Being a data scientist isn’t solely about writing code and developing data models. General knowledge of organizational goals and challenges can help you to start asking the right questions and develop useful queries. 

Analytical Reasoning

Due to the technical nature of the position, possessing analytical and critical thinking skills is necessary for success. Working with data is all about identifying patterns and thinking quantitatively. Data scientists need to be able to look at any particular problem objectively in order to come to logical conclusions.

Scientists should also be considering different angles and perspectives when looking at their data. Constantly asking questions and deriving insights from various points of view are crucial to driving effective and objective analysis.

Curiosity

A data scientist’s abilities should extend far beyond their technical expertise. Soft skills such as curiosity and creativity are what distinguish good scientists from great ones. The job title contains the word ‘scientist’ for a reason, not all of the answers are known! You will need to theorize, develop hypotheses, experiment, and ultimately draw conclusions on a day to day basis. 

Curiosity is needed when it comes to handling these tasks and diving deeper into the complex problems at hand. Great scientists should take an iterative approach to understand their data and be open to questioning their initial assumptions. Highly effective data scientists are always looking for the “why” and “how” behind the data in order to probe for additional information. Exploration and experimentation are vital to producing conclusive insights. 

Communication

Furthermore, another essential skill for data scientists is communication. One of the primary responsibilities of a data scientist is to communicate their methods and findings to other business units. With the abundance of data today, it can be messy and difficult to understand, especially if you are entirely unfamiliar with the data. 

It’s important to communicate insights in a way that’s easy for others to understand and drive decisions from. After all, what good is the data if no one can understand it? Any skills surrounding storytelling abilities or communication will help to properly inform key stakeholders on their queries. 

Collaboration

Whether you are working within your department or with others to collect and communicate data, collaboration is critical. Much like every job, teamwork plays a vital role in maximizing productivity. This working relationship, though, is especially important for data scientists as there is often a disconnect between business units and data science teams. Data scientists help to bridge the gap between the two business functions and allow for greater effectiveness all around.

Conclusion

Overall, many traits can contribute to the success of a data scientist. But it’s important to note that none of these traits are necessarily required or set in stone when it comes to the makings of an effective one. The role contains a number of unique responsibilities but there remains an opportunity to make it entirely your own. Consider these traits as a common data scientist’s keys or guide to data mastery when developing in their career. 

Categories
Big Data Data Analytics Data Preparation Data Science

The Beginner’s Guide to Data Streaming

What is Data Streaming?

Data streaming is when small bits of data are sent consistently, typically through multiple channels. Over time, the amount of data sent often amounts to terabytes and would be too overwhelming for manual evaluation. While everything can be digitally sent in real-time, it’s up to the software using it to filter what’s displayed. 

Data streaming is often utilized as an alternative to a periodic, batch data dump approach. Instead of grabbing data at set intervals, streamed data is received nearly as soon as it’s generated. Although the buzzword is often associated with watching videos online, that is only one of many possible implementations of the technology.

How is Data Streaming Used?

Keep in mind that any form of data may be streamed. This makes the possibilities involving data streaming effectively limitless. It’s proven to be a game-changer for Business Analytics systems and more. From agriculture to the fin-tech sector to gaming, it’s used all over the web.

One common industry application of data streaming is in the transportation and logistics field. Using this technology, managers can see live supply chain statistics. In combination with artificial intelligence, potential roadblocks can be detected after analysis of streamed data and alternative approaches can be taken so deadlines are always met.

Data streaming doesn’t only benefit employees working in the field. Using Business Analytics tools, administrators, and executives can easily see real-time data or analyze data from specific time periods. 

Why Should We Use Data Streaming?

Data silos and disparate data sources have plagued the industry for countless years. Data streaming allows real-time, relevant information to be displayed to those who need access to it the most. Rather than keeping an excessive amount of data tucked away on a server rarely accessed, this technology puts decision-driving information at the forefront.

Previously, this type of real-time view of business processes was seen as impossible. Now that it’s possible to have an internet connection almost everywhere, cloud computing makes live data streaming affordable, and Business Analytics tools are ready to implement data streaming, there’s no reason it would be inaccessible.  

While it may be tempting to stick to older ways of processing data, companies who don’t adapt to this new standard will likely find it more difficult to remain competitive over the years. Companies that do incorporate the technology will likely see their operations become more streamlined and find it much easier to analyze and adjust formerly inefficient processes.

Categories
Data Analytics Data Science Data Science Careers

Data Scientist vs. Data Analyst: What’s the Difference?

In today’s business climate, data is the one thing everyone is looking to as a means to compete and drive better decision making across business units. But who in your organization is actually working with data and putting it to work? You’ve likely seen an abundance of job listings for data analysts and data scientists alike or may even currently be in one of these roles yourself. These positions are becoming increasingly essential across industries, the Harvard Business Review even deemed data scientist as the “sexiest job” of the 21st century.

However, the lines can often be blurred between the roles of data scientists and data analysts. So now that we’ve established the rising demand and importance of these common positions, let’s take a closer look at understanding each. Let’s explore what it means to be a data scientist or an analyst as well as some key distinctions to keep in mind. 

What do Data Analysts do?

Data analysts are versatile in their role and are predominantly focused on driving business decisions. It’s common for data analysts to start with specific business questions that need to be answered. 

Some common job functions for data analysts include:

  • Identify trends and analyze data to develop insights
  • Design business intelligence dashboards
  • Create reports based on their findings
  • Communicate insights to business stakeholders

What do Data Scientists do?

While a data scientist also works with data thoroughly to develop insights and communicate them to stakeholders, they commonly apply more technical aspects to their work. This includes things such as coding, building algorithms, and developing experiments. Data scientists must know how to collect, clean, and handle data throughout the pipeline. 

Data scientists typically require more experience and advanced qualifications, specifically when it comes to their knowledge of statistical computer languages such as Python and SQL. However, there is far more to a data scientist’s role than merely their technical expertise. They have to be able to ask the right questions and streamline all aspects of the data pipeline.

Some common tasks and responsibilities for data scientists include:

  • Build and manipulate data models
  • Optimize data collection from disparate data sources
  • Clean data and create processes to maintain data quality
  • Develop algorithms and predictive models

What’s the Difference?

While both roles have data in common, the primary difference between the two is how they work with data. Data scientists focus on the entire data lifecycle, from collection and cleaning to final insights and interpretation. A data analysts’ role is weighted at the end of the pipeline, this being the interpretation of data and communicating findings to business units.

It’s not uncommon for data analysts to transition into the role of data scientist later on in their careers. Many view data analysts as a stepping stone, where analysts are able to practice the foundational tools of working with data.

How Much do Data Scientists and Data Analysts Make?

Your second thought after “show me the data” is probably “show me the money!” Now that we’ve reviewed the similarities and differences in each role’s responsibilities, let’s get down to the numbers. According to Glassdoor, you can expect to earn an average base pay of around $113,309 as a Data Scientist. This is nearly double the average base pay for a Data Analyst which comes in at around $62,453 per year. The seemingly drastic pay difference primarily reflects the variation in technical expertise and years of experience needed.

Conclusion

Overall, there is no predetermined definition of what it means to be a data analyst or a data scientist. Each role is unique and will vary depending on the industry, there are also a number of other factors specific to each organization. Though, it’s important to remember that there is room to make the position your own and define either job title for yourself. 

Categories
Big Data Business Intelligence Data Science

ETL vs. ELT: Critical Differences to Know

ETL and ELT are processes for moving data from one system to another. Both processes involve the same 3 steps, Extraction, Transformation, and Loading. The fundamental difference between the two lies in the order in which the data is loaded into the data warehouse and analyzed.

What is ETL?

ETL has been the traditional method for data warehousing and analytics. It is used to synthesize data from more than one source in order to build a data warehouse or data lake. First, the data is extracted from RDBMS source systems, which is the extraction stage. Next is the transformation stage, where all transformations are applied to the extracted data, and only then is it loaded into the end-target system to be analyzed by business intelligence tools.

What is ELT?

ELT involves the same three steps as ETL, but in ELT, the data is loaded immediately after extraction, before the transformation stage. With ELT, all data sources are aggregated into a single, centralized repository. With today’s cloud based data warehouses being scalable and separating storage from compute resources, ELT makes more sense for most modern businesses. ELT allows for unlimited access to all of your data by multiple users at the same time, saving both time and effort.

Benefits of ELT

Simplicity: Transformations in the data warehouse are generally written in SQL, which is the traditional language for most data applications. This means that anyone who knows SQL can contribute to the transformation of the data.

Speed: All of the data is stored in the warehouse and will be available whenever it is needed. Analysts do not have to worry about structuring the data before loading it into the warehouse. 

Self service analytics: When all of your data is linked together in your data warehouse you can then easily use BI tools to drill down from an aggregated summary of the data to the individual values underneath.

Bug Fixes: If you discover any errors in your transformation pipeline, you can simply fix the bug and re-run just the transformations with no harm done. With ETL however, the entire process would need to be redone.